code: def test_custom_transducer_trainable_and_decodable(
      train_dic, recog_dic
  ):
      train_args = make_train_args(**train_dic)
      recog_args = make_recog_args(**recog_dic)
  
      model, feats, feats_len, labels, data, uttid_list = prepare(
          train_args
      )
  
      optim = torch.optim.Adam(model.parameters(), 0.01)
      loss = model(feats, feats_len, labels)
  
      optim.zero_grad()
      loss.backward()
      optim.step()
  
      beam_search = BeamSearchTransducer(
          decoder=model.decoder,
          joint_network=model.transducer_tasks.joint_network,
          beam_size=recog_args.beam_size,
          lm=recog_args.rnnlm,
          lm_weight=recog_args.lm_weight,
          search_type=recog_args.search_type,
          max_sym_exp=recog_args.max_sym_exp,
          u_max=recog_args.u_max,
          nstep=recog_args.nstep,
          prefix_alpha=recog_args.prefix_alpha,
          score_norm=recog_args.score_norm_transducer,
          softmax_temperature=recog_args.softmax_temperature,
      )
  
      with torch.no_grad():
          nbest = model.recognize(
              feats[0, : feats_len[0]].numpy(), beam_search
          )
  
          print(nbest[0]["yseq"][1:-1])
  
imports: import argparse
  import json
  import tempfile
  import pytest
  import torch
  from packaging.version import parse
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.asr.pytorch_backend.asr_init import load_trained_model
  from espnet.nets.beam_search_transducer import BeamSearchTransducer
  from espnet.nets.pytorch_backend.e2e_asr_transducer import E2E
  from espnet.nets.pytorch_backend.transducer.blocks import build_blocks
label_desc: Use image node_id 7 for calling a global function with example usage: test_custom_transducer_trainable_and_decodable(train_dic, recog_dic) without return types